Open AccessSemi-supervised classification method of SAR images using spectral clustering in contourlet domain
Author(s) -
Kaiwen Jiang,
Degan Zhang,
Haixia Xu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1486/4/042032
Subject(s) - contourlet , particle swarm optimization , pattern recognition (psychology) , spectral clustering , artificial intelligence , eigenvalues and eigenvectors , cluster analysis , computer science , mathematics , domain (mathematical analysis) , similarity (geometry) , algorithm , image (mathematics) , wavelet , mathematical analysis , physics , wavelet transform , quantum mechanics
A new based on Semi-supervised classification theory for SAR images in contourlet domain is proposed, in this paper. Attempting to get better and faster performance, the PSO algorithm (Particle swarm optimization algorithm) and contourlet domain is proposed to instead of traditional k-means algorithm. PSO is used to find the global optimum by performing a global search in the whole solution space. And then, contourlet is applied in front of construct the similarity matrix to extract more effective eigenvalues. In section five, the proposed algorithm got better classification results than the traditional k-means algorithm which is proved by experimental results show that in terms of running time, classification accuracy and Kappa coefficient.